Dzmitry Bahdanau (ServiceNow Research)
Alexandra Birch (University of Edinburgh)
Kyunghyun Cho (New York University & Prescient Design)
Marzieh Fadaee (Cohere For AI)
Benoît Favre (Aix-Marseille University, LIS, CNRS, LIG)
Dirk Hovy (Bocconi University). From Words to Culture: Unleashing NLP for Social Science Insights
[Abstract]
The rapid development of large language models in recent years has transformed the field of NLP. Many people are concerned that it has trivialized the field or even rendered it obsolete.
In this talk, I’ll argue that neither is true: NLP has a long way to go, and LLMs are the most recent in a long line of methods that have advanced the field. LLMs have freed us from many of the nitty-gritty details that previously hampered NLP research, allowing us to focus on larger and more interesting questions.
Computational social science is one area that still has many interesting questions and research projects to pursue. There is a lot that NLP models can tell us about language, and that language can tell us about society. From relatively simple text classification approaches for sentiment and social media analysis to capture political shifts and company culture; latent structure discovery through clustering-related approaches for socio-linguistic developments; hidden societal biases about religion, gender, socio-economic status, and emotions; the ability to reframe political arguments to address content moderation; prototyping survey studies, inferring missing demographic groups, and deepening the response validity; to comparing human and algorithmic preferences and what that tells us about being human.
This talk will provide a comprehensive practical guide for aspiring computational social scientists interested in using NLP to work with text data. We will also discuss the limitations of LLMs regarding social interaction, the implications for their safety and utility, and what this means for the future of NLP research.
François Yvon (CNRS, ISIR). Text Generation: Know Your Options !
[Abstract]
Text generation, contextual or non-contextual, is ubiquitous in the
current LLM era, as it serves as the most basic block in multiple
application contexts, from question answering and dialog systems to text
summarization and machine translation, and many more. Generation is thus
equally useful to compute deterministic and highly non-deterministic
mappings with various level of output constraints. Furthermore, text
generation is also used as a sub-routine of more complex generation
strategies, aiming to produce syntactically well-formed (e.g. for code
generation) or semantically consistent outputs, possibility through
multiple steps of generation (e.g, in chain-of-thoughts generation) or
to collect diverse samples from the generating distribution. To cover
this considerable diversity of uses, multiple text generation strategies
have been proposed, some less well-known than others. In this talk I
will review various families of generation algorithms, from the most
basic ones to the more sophisticated approaches, so as to document, as
much as possible, the possible options that are available to text
generation users.
Speakers from previous editions are here (2024), here (2023), here (2022) and here (2021)